Dropbox's engineers now accept more than one million lines of AI-suggested code every month with Cursor.

"Speed is the only advantage of any company," says Ali Dasdan, Dropbox's chief technology officer. He and the Dropbox team recognized early on that being slow to adopt AI was a bigger threat than moving too soon. AI needed to be embedded across the software development lifecycle if Dropbox was going to achieve the velocity it wanted.

But that meant deploying new coding tools inside an unusually demanding environment. Dropbox runs its own data centers that serve more than 300,000 requests per second and thousands of engineers who maintain a monorepo with more than 550,000 files. Dasdan knew that only an AI system that could reason across the entire codebase, and was widely adopted, would end up delivering the velocity gains Dropbox was aiming for.

#Organic adoption

By 2024, Dropbox engineers had already started experimenting with Cursor. Initially they shared what they were learning through informal channels, such as Slack conversations and quick internal write-ups. Dasdan noticed the activity and nurtured it by creating a group of AI champions. He helped them amplify the way they were working while removing barriers to adoption.

The effect was immediate. As access became easier, more engineers tried the tools, shared what they were learning, and adoption accelerated on its own.

#Leaders try the tools

The next step-change took place in April 2025 when Dropbox held a company-wide hackathon. The biggest impact was on Dasdan himself. He wanted to build a "smart finder" for his hack week project, but other work intervened and the night before it was due, he still hadn't started. That's when he tried Cursor for the first time.

Dasdan shared his experience in a presentation to a group of CTOs he meets with regularly. He was surprised to learn many of them still had not tried AI coding tools. His message to them was simple.

#Indexing the monorepo

Once interest in Cursor moved beyond individual experiments, the next question was whether it could handle Dropbox's entire monorepo.

Every Cursor deployment begins the same way: by indexing the codebase. Cursor scans each file that isn't ignored, breaks the code into structured chunks, and generates embeddings that capture how those pieces relate to one another. The result is a semantic index that the models use when generating or editing code.

At Dropbox's scale, this step was critical. Indexing gave Cursor the context it needed to follow the codebase's structure and generate changes that fit naturally within it. It also made the codebase more accessible to Dropbox engineers themselves. Through Cursor, they gained a clearer map of how different parts of the codebase fit together, empowering leaders and letting new hires ramp faster.

#A measurable impact on velocity

More than 90 percent of Dropbox engineers now use AI tools weekly, with Cursor as a primary driver of that activity.

The effects have been almost immediate. Dropbox measures engineering performance through an internal framework that emphasizes speed, effectiveness, and quality. Since adopting Cursor, PR throughput and cycle time have moved into the upper tier of industry benchmarks.

Engineers feel the change in their day-to-day work. Cursor appears in nearly every step of development, from writing and reviewing code to testing, documentation, and migrations. They can move through the codebase faster, reinforcing the principle that set Dropbox's Cursor adoption in motion: speed is everything.

If you're interested in embedding AI in every phase of the software development lifecycle, please reach out to our team to get started with a Cursor trial.